Valorant Stats Analysis

This project performs a comprehensive analysis of the Valorant game dataset from Kaggle. The dataset is used to answer key questions, apply machine learning techniques, and visualize insights about the game and its agents. Below are the key highlights and steps involved in the project:

Project Structure

• Dataset: The Valorant dataset from Kaggle, contains data on agents, maps, players, teams, and weapons.
• Analysis Methods: Various statistical and machine learning techniques are applied, including:
  • Identifying the top 10 most popular agents in Valorant.
  • Applying Linear Regression for predictive modeling.
  • Building a Decision Tree classifier for both datasets.
  • Visualizing data insights using heatmaps.
  • K-Means Clustering for pattern discovery.
  • Outlier Detection for identifying anomalies.

Project Goals

1. Top 10 Most Popular Agents:

   • Analyze the dataset to identify and rank the most frequently used agents in Valorant.

2. Linear Regression Analysis:

   • Apply linear regression to identify relationships between game statistics (like player performance) and other variables.

3. Decision Tree Classifier:

   • Build a decision tree to predict outcomes and make data-driven decisions based on dataset features.

4. Heatmap Visualizations:

   • Create heatmaps to visualize correlations between features in the dataset, providing insights into patterns and relationships.

5. K-Means Clustering:

   • Use K-Means to group players, teams, or agents into clusters based on their stats, identifying common patterns or playstyles.

6. Outlier Detection:

   • Identify outliers in the data to detect unusual player performances, match results, or agent selection patterns.

Tech Stack

• Python
• Pandas, NumPy (Data Processing)
• Scikit-learn (Linear Regression, Decision Tree, K-Means, Outlier Detection)
• Seaborn, Matplotlib (Data Visualization)

How to Run

1. Clone the repository:

   git clone https://github.com/your-username/valo-stats-analysis.git

2. Install required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter notebooks or Python scripts to view analysis results.

4. Run the ValoPlayer'sStats.ipynb (Main file of program)

Contributing

Feel free to fork the repository, submit issues, or contribute via pull requests.